Computer automation enables analysis of large amounts of data that previously was not practical to perform. As computer technology improves, so too does the amount of data that may be analyzed, and the complexity of the analysis that may be performed. For certain types of analyses, computer performance has increased to the point where the analysis may be performed substantially in real time, enabling interactive online analysis.
One type of analysis now enhanced by the present improvements in computer performance is network analysis. Where many analyses require predefining relationships in the subject data, data mining instead detects those relationships.
There are many techniques to detect relationships. These include dimension reduction. Similarly, there are many algorithms to implement these techniques. For example, dimension reduction may be implemented via a genetic algorithm or in the alternative through matrix operations, specifically matrix reduction algorithms. The implementation of an algorithm may further vary depending on the application. For example, the implementation for analysis of a social network likely would differ from the analysis of a computer network.
In network analysis, a form of data mining, network analysis algorithms may be component algorithms of a larger network algorithm. In these situations, component algorithms are called sub-algorithms. As in with data mining in general, network analysis algorithms typically do not definitively state whether or not a relationship exists. Rather, network analysis reports the likelihood that a relationship exists, and accordingly much of network analysis is inherently statistical in nature. Presently, network analysis is applied to larger and larger amounts of data.
Reporting the aggregated results of a large corpus of data is most easily accomplished via a visual representation. While many visual representations exist, the most typical representation is some variation of a network map. A network map is a graph where each datum subjected to analysis is represented as a node and each relationship is represented as an edge.
Presently, there is no consolidated automated framework to construct complex network analysis applications or to address related issues.